Classification of MODIS time series with Dense Bag-of-Temporal-SIFT-Words: Application to cropland mapping in the Brazilian Amazon

Adeline Bailly; Damien Arvor; Laetitia Chapel; Romain Tavenard

doi:10.1109/IGARSS.2016.7729594

Classification of MODIS time series with Dense Bag-of-Temporal-SIFT-Words: Application to cropland mapping in the Brazilian Amazon

Adeline Bailly
, Damien Arvor
, Laetitia Chapel
, Romain Tavenard

Université de Rennes 2
IRDL

Résultats de recherche: Le chapitre dans un livre, un rapport, une anthologie ou une collection › Contribution à une conférence › Revue par des pairs

4 Citations (Scopus)

Résumé

Mapping croplands is a challenging problem in a context of climate change and evolving agricultural calendars. Classification based on MODIS vegetation index time series is performed in order to map crop types in the Brazilian state of Mato Grosso. We used the recently developed Dense Bag-of-Temporal-SIFT-Words algorithm, which is able to capture temporal locality of the data. It allows the accurate detection of around 70% of the agricultural areas. It leads to better classification rates than a baseline algorithm, discriminating more accurately classes with similar profiles.

langue originale	Anglais
titre	2016 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2016 - Proceedings
Editeur	Institute of Electrical and Electronics Engineers Inc.
Pages	2300-2303
Nombre de pages	4
ISBN (Electronique)	9781509033324
Les DOIs	https://doi.org/10.1109/IGARSS.2016.7729594
état	Publié - 1 nov. 2016
Modification externe	Oui
Evénement	2016 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2016 - Beijing, Chine Durée: 10 juil. 2016 → 15 juil. 2016

Série de publications

Nom	International Geoscience and Remote Sensing Symposium (IGARSS)
Volume	2016-November
ISSN (Electronique)	2153-7003

Une conférence

Une conférence	2016 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2016
Pays/Territoire	Chine
La ville	Beijing
période	10/07/16 → 15/07/16

SDG des Nations Unies

Ce résultat contribue à ou aux Objectifs de développement durable suivants

SDG 13 Action climatique

Accès au document

10.1109/IGARSS.2016.7729594

Autres fichiers et liens

Lien vers la publication dans Scopus

Contient cette citation

Bailly, A., Arvor, D., Chapel, L., & Tavenard, R. (2016). Classification of MODIS time series with Dense Bag-of-Temporal-SIFT-Words: Application to cropland mapping in the Brazilian Amazon. Dans 2016 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2016 - Proceedings (p. 2300-2303). Article 7729594 (International Geoscience and Remote Sensing Symposium (IGARSS) ; Vol 2016-November). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IGARSS.2016.7729594

Bailly, Adeline ; Arvor, Damien ; Chapel, Laetitia et al. / Classification of MODIS time series with Dense Bag-of-Temporal-SIFT-Words : Application to cropland mapping in the Brazilian Amazon. 2016 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2016 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2016. p. 2300-2303 (International Geoscience and Remote Sensing Symposium (IGARSS) ).

@inproceedings{6213b0bead894c73b86fe1133dc508ee,

title = "Classification of MODIS time series with Dense Bag-of-Temporal-SIFT-Words: Application to cropland mapping in the Brazilian Amazon",

abstract = "Mapping croplands is a challenging problem in a context of climate change and evolving agricultural calendars. Classification based on MODIS vegetation index time series is performed in order to map crop types in the Brazilian state of Mato Grosso. We used the recently developed Dense Bag-of-Temporal-SIFT-Words algorithm, which is able to capture temporal locality of the data. It allows the accurate detection of around 70\% of the agricultural areas. It leads to better classification rates than a baseline algorithm, discriminating more accurately classes with similar profiles.",

keywords = "D-BoTSW, MODIS, Time Series Classification, Vegetation Index",

author = "Adeline Bailly and Damien Arvor and Laetitia Chapel and Romain Tavenard",

note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 2016 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2016 ; Conference date: 10-07-2016 Through 15-07-2016",

year = "2016",

month = nov,

day = "1",

doi = "10.1109/IGARSS.2016.7729594",

language = "English",

series = "International Geoscience and Remote Sensing Symposium (IGARSS) ",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "2300--2303",

booktitle = "2016 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2016 - Proceedings",

}

Bailly, A, Arvor, D, Chapel, L & Tavenard, R 2016, Classification of MODIS time series with Dense Bag-of-Temporal-SIFT-Words: Application to cropland mapping in the Brazilian Amazon. Dans 2016 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2016 - Proceedings., 7729594, International Geoscience and Remote Sensing Symposium (IGARSS) , VOL. 2016-November, Institute of Electrical and Electronics Engineers Inc., p. 2300-2303, 2016 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2016, Beijing, Chine, 10/07/16. https://doi.org/10.1109/IGARSS.2016.7729594

Classification of MODIS time series with Dense Bag-of-Temporal-SIFT-Words: Application to cropland mapping in the Brazilian Amazon. / Bailly, Adeline; Arvor, Damien; Chapel, Laetitia et al.
2016 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2016 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2016. p. 2300-2303 7729594 (International Geoscience and Remote Sensing Symposium (IGARSS) ; Vol 2016-November).

Résultats de recherche: Le chapitre dans un livre, un rapport, une anthologie ou une collection › Contribution à une conférence › Revue par des pairs

TY - GEN

T1 - Classification of MODIS time series with Dense Bag-of-Temporal-SIFT-Words

T2 - 2016 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2016

AU - Bailly, Adeline

AU - Arvor, Damien

AU - Chapel, Laetitia

AU - Tavenard, Romain

PY - 2016/11/1

Y1 - 2016/11/1

N2 - Mapping croplands is a challenging problem in a context of climate change and evolving agricultural calendars. Classification based on MODIS vegetation index time series is performed in order to map crop types in the Brazilian state of Mato Grosso. We used the recently developed Dense Bag-of-Temporal-SIFT-Words algorithm, which is able to capture temporal locality of the data. It allows the accurate detection of around 70% of the agricultural areas. It leads to better classification rates than a baseline algorithm, discriminating more accurately classes with similar profiles.

AB - Mapping croplands is a challenging problem in a context of climate change and evolving agricultural calendars. Classification based on MODIS vegetation index time series is performed in order to map crop types in the Brazilian state of Mato Grosso. We used the recently developed Dense Bag-of-Temporal-SIFT-Words algorithm, which is able to capture temporal locality of the data. It allows the accurate detection of around 70% of the agricultural areas. It leads to better classification rates than a baseline algorithm, discriminating more accurately classes with similar profiles.

KW - D-BoTSW

KW - MODIS

KW - Time Series Classification

KW - Vegetation Index

UR - https://www.scopus.com/pages/publications/85007415768

U2 - 10.1109/IGARSS.2016.7729594

DO - 10.1109/IGARSS.2016.7729594

M3 - Conference contribution

AN - SCOPUS:85007415768

T3 - International Geoscience and Remote Sensing Symposium (IGARSS)

SP - 2300

EP - 2303

BT - 2016 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2016 - Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

Y2 - 10 July 2016 through 15 July 2016

ER -

Bailly A, Arvor D, Chapel L, Tavenard R. Classification of MODIS time series with Dense Bag-of-Temporal-SIFT-Words: Application to cropland mapping in the Brazilian Amazon. Dans 2016 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2016 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2016. p. 2300-2303. 7729594. (International Geoscience and Remote Sensing Symposium (IGARSS) ). doi: 10.1109/IGARSS.2016.7729594

Classification of MODIS time series with Dense Bag-of-Temporal-SIFT-Words: Application to cropland mapping in the Brazilian Amazon

Résumé

Série de publications

Une conférence

SDG des Nations Unies

Accès au document

Autres fichiers et liens

Empreinte digitale

Contient cette citation